Learning Analytics for Improving Learning Outcomes in Data-Driven Education
DOI:
https://doi.org/10.54518/nes.3.2.2025.1354Keywords:
Data-Driven Education, Digital Learning, Learning Outcomes, Learning Analytics, Personalized LearningAbstract
Digital transformation in education has encouraged the utilization of learning data as a foundation for more effective decision-making. One of the emerging approaches is Learning Analytics, which refers to the process of collecting, analyzing, and interpreting learning data to understand and optimize students' learning processes. This article aims to analyze the role of Learning Analytics in improving learning outcomes through a data-driven learning approach. This study employed a descriptive qualitative method using a literature review of scientific publications from the last five years related to the implementation of Learning Analytics in educational contexts. The findings indicate that Learning Analytics contributes to improving learning engagement, personalized learning, feedback provision, early identification of academic risks, and evidence-based pedagogical decision-making. However, its implementation still faces challenges related to data privacy, information security, technological infrastructure readiness, and educators' data literacy competencies. Therefore, optimizing Learning Analytics requires ethical data management strategies, continuous development of user competencies, and sustainable technology integration to support effective learning processes and improve educational outcomes.
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